kmeans手肘法代码matlab
时间: 2023-08-23 13:17:48 浏览: 102
以下是使用MATLAB实现k-means算法的手肘法代码:
```matlab
clc; clear; load fisheriris
data = meas; % 对原始数据进行归一化处理
data = mapminmax(meas, 0, 1);
\[n, p\] = size(data);
K = 8;
D = zeros(K, 2);
for k = 2:K
\[lable, c, sumd, d\] = kmeans(data, k, 'dist', 'sqeuclidean');
sse1 = sum(sumd.^2);
D(k, 1) = k;
D(k, 2) = sse1;
end
plot(D(2:end, 1), D(2:end, 2))
hold on;
plot(D(2:end, 1), D(2:end, 2), 'or');
title('不同K值聚类偏差图')
xlabel('分类数(K值)')
ylabel('簇内误差平方和')
```
这段代码使用了鸢尾花数据集作为示例数据,你可以根据自己的数据进行替换。代码的目的是通过手肘法确定k-means算法的最佳聚类类别数。它计算了不同k值下的簇内误差平方和,并绘制了聚类偏差图。根据图形的变化趋势,可以选择一个合适的k值作为最佳聚类类别数。
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